Metabolomics in Diagnosis and Prognosis of Diabetic Kidney Disease
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Abstract
Diabetic kidney disease (DKD) emerges as a significant contributor to end-stage kidney disease. Currently, the diagnostic approach relies on a comprehensive clinical history and identifying notable albuminuria, accompanied by a sequential decline in glomerular filtration rate. However, this method may unveil substantial impairment in kidney function at the time of diagnosis, prompting a search for innovative biomarkers to overcome this limitation. “Metabolomics,” a systematic and quantitative analysis of all metabolites in biological samples, has emerged as a viable solution. Metabolomics investigates metabolites resulting from internal metabolic processes and external factors such as diet and the gut microbiome. Metabolomics has undergone significant advancements, leveraging technologies such as Mass Spectrometry and Nuclear Resonance Spectroscopy, coupled with sophisticated data analysis methods. This progress has led to the identification of novel metabolites, including amino acids, carbohydrates, lipids, and nucleic acids, thereby offering promising prospects for the diagnosis and prognosis of DKD. Furthermore, recognized metabolites provide insights into critical disease mechanisms, such as mitochondrial dysfunction in DKD. The evolving field of Metabolomics has deepened our understanding of DKD, contributing to enhanced diagnostic precision, prognostic capabilities, and more personalized treatment strategies for individuals with DKD.
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